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Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics

René Breuer, Manuel Mattheisen, Josef Frank, Bertram Krumm, Jens Treutlein, Layla Kassem, Jana Strohmaier, Stefan Herms, Thomas W. Mühleisen, Franziska Degenhardt, Sven Cichon, Markus M. Nöthen, George Karypis, John Kelsoe, Tiffany Greenwood, Caroline Nievergelt, Paul Shilling, Tatyana Shekhtman, Howard Edenberg, David Craig, Szabolcs Szelinger, John Nurnberger, Elliot Gershon, Ney Alliey-Rodriguez, Peter Zandi, Fernando Goes, Nicholas Schork, Erin Smith, Daniel Koller, Peng Zhang, Judith Badner, Wade Berrettini, Cinnamon Bloss, William Byerley, William Coryell, Tatiana Foroud, Yirin Guo, Maria Hipolito, Brendan Keating, William Lawson, Chunyu Liu, Pamela Mahon, Melvin McInnis, Sarah Murray, Evaristus Nwulia, James Potash, John Rice, William Scheftner, Sebastian Zöllner, Francis J. McMahon, Marcella Rietschel, Thomas G. Schulze
International Journal of Bipolar Disorders, Volume 6, Issue 1, 2018
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Abstract
Background
Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.

Results
Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio?~?3.4 and 3.0, respectively) and support previously reported molecular biological findings.

Conclusion
Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
Research topics: Bioinformatics | Data mining